Load all required libraries.

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.6.3
## -- Attaching packages ------------------------------------------------------------------ tidyverse 1.3.0 --
## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.0
## v tidyr   1.1.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## Warning: package 'ggplot2' was built under R version 3.6.3
## Warning: package 'tibble' was built under R version 3.6.3
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## Warning: package 'dplyr' was built under R version 3.6.3
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## -- Conflicts --------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(plotly)
## Warning: package 'plotly' was built under R version 3.6.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(broom)
## Warning: package 'broom' was built under R version 3.6.3

Read in raw data from RDS.

raw_data <- readRDS("./n1_n2_cleaned_cases.rds")

Make a few small modifications to names and data for visualizations.

final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
  rename(Facility = wrf) %>%
  mutate(Facility = recode(Facility, 
                           "NO" = "WRF A",
                           "MI" = "WRF B",
                           "CC" = "WRF C"))

Seperate the data by gene target to ease layering in the final plot

#make three data layers
only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>% 
  select(c(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke)) %>%
  group_by(date) %>% summarise_if(is.numeric, mean)

#specify fun colors
background_color <- "#7570B3"
seven_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02')
#remove facilty C for now
only_n1 <- only_n1[!(only_n1$Facility == "WRF C"),]
only_n2 <- only_n2[!(only_n2$Facility == "WRF C"),]

Build the main plot

      #first layer is the background epidemic curve
        p1 <- only_background %>%
              plotly::plot_ly() %>%
              plotly::add_trace(x = ~date, y = ~new_cases_clarke, 
                                type = "bar", 
                                hoverinfo = "text",
                                text = ~paste('</br> Date: ', date,
                                                     '</br> Daily Cases: ', new_cases_clarke),
                                alpha = 0.5,
                                name = "Daily Reported Cases",
                                color = background_color,
                                colors = background_color,
                                showlegend = FALSE) %>%
            layout(yaxis = list(title = "Clarke County Daily Cases", showline=TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #renders the main plot layer two as seven day moving average
        p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X7_day_ave_clarke, 
                             type = "scatter",
                             mode = "lines",
                             hoverinfo = "text",
                            text = ~paste('</br> Date: ', date,
                                                     '</br> Seven-Day Moving Average: ', X7_day_ave_clarke),
                             name = "Seven Day Moving Average Athens",
                             line = list(color = seven_day_ave_color),
                             showlegend = FALSE)
      

        
        #renders the main plot layer three as positive target hits
        
        p2 <- plotly::plot_ly() %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n1,
                                       symbol = ~Facility,
                                       marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n2,
                                       symbol = ~Facility,
                                       marker = list(color = '#D95F02', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
            layout(yaxis = list(title = "SARS CoV-2 Copies/L", 
                                 showline = TRUE,
                                 type = "log",
                                 dtick = 1,
                                 automargin = TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #adds the limit of detection dashed line
        p2 <- p2 %>% plotly::add_segments(x = as.Date("2020-03-14"), 
                                          xend = ~max(date + 10), 
                                          y = 3571.429, yend = 3571.429,
                                          opacity = 0.35,
                                          line = list(color = "black", dash = "dash")) %>%
          layout(annotations = list(x = as.Date("2020-03-28"), y = 3.8, xref = "x", yref = "y", 
                                    text = "Limit of Detection", showarrow = FALSE))

        

        p1
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning: Ignoring 1 observations
        p2
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Combine the two main plot pieces as a subplot

p_combined <-
    plotly::subplot(p2,p1, # plots to combine, top to bottom
      nrows = 2,
      heights = c(.6,.4),  # relative heights of the two plots
      shareX = TRUE,  # plots will share an X axis
      titleY = TRUE
    ) %>%
    # create a vertical "spike line" to compare data across 2 plots
    plotly::layout(
      xaxis = list(
        spikethickness = 1,
        spikedash = "dot",
        spikecolor = "black",
        spikemode = "across+marker",
        spikesnap = "cursor"
      ),
      yaxis = list(spikethickness = 0)
    )
## Warning: Ignoring 1 observations
p_combined

Save the plot to pull into the index

save(p_combined, file = "./plotly_fig.rda")

Save an htmlwidget for website embedding

htmlwidgets::saveWidget(p_combined, "plotly_fig.html")

Build loess smoothing figures figures

#create smoothing data frames 
#n1
smooth_n1 <- only_n1 %>% select(-c(Facility)) %>% 
  group_by(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke) %>%
  summarize(sum_copy_num_L = sum(mean_total_copies)) %>%
  ungroup() %>%
  mutate(log_sum_copies_L = log10(sum_copy_num_L)) %>%
  mutate(target = "N1")
## `summarise()` regrouping output by 'date', 'cases_cum_clarke', 'new_cases_clarke', 'X7_day_ave_clarke' (override with `.groups` argument)
#n2
smooth_n2 <- only_n2 %>% select(-c(Facility)) %>% 
  group_by(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke) %>%
  summarize(sum_copy_num_L = sum(mean_total_copies)) %>%
  ungroup() %>%
  mutate(log_sum_copies_L = log10(sum_copy_num_L)) %>%
  mutate(target = "N2")
## `summarise()` regrouping output by 'date', 'cases_cum_clarke', 'new_cases_clarke', 'X7_day_ave_clarke' (override with `.groups` argument)
#add trendlines 
#extract data from geom_smooth
#n1 extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_n1 <- ggplot(smooth_n1, aes(x = date, y = log_sum_copies_L)) + 
  stat_smooth(aes(outfit=fit_n1<<-..y..), method = "loess", color = '#1B9E77', 
              span = 0.6, n = 106)
## Warning: Ignoring unknown aesthetics: outfit
#n2 extract
extract_n2 <- ggplot(smooth_n2, aes(x = date, y = log_sum_copies_L)) + 
  stat_smooth(aes(outfit=fit_n2<<-..y..), method = "loess", color = '#1B9E77', 
              span = 0.6, n = 106)
## Warning: Ignoring unknown aesthetics: outfit
#look at the fits to align dates and total observations
#n1
extract_n1
## `geom_smooth()` using formula 'y ~ x'

fit_n1
##   [1] 10.76551 10.98297 11.19222 11.39319 11.58582 11.77005 11.94582 12.11306
##   [9] 12.27148 12.42101 12.56197 12.69469 12.81948 12.93666 13.04655 13.15047
##  [17] 13.24888 13.34076 13.42514 13.50102 13.56739 13.62326 13.66719 13.69911
##  [25] 13.72007 13.73112 13.73331 13.72771 13.71537 13.68554 13.63173 13.56138
##  [33] 13.48190 13.40072 13.32526 13.26295 13.19875 13.11742 13.02610 12.93189
##  [41] 12.84192 12.76333 12.70322 12.63895 12.55050 12.44894 12.34538 12.25088
##  [49] 12.17654 12.13344 12.10863 12.08396 12.06263 12.04788 12.04294 12.05103
##  [57] 12.07537 12.13390 12.23322 12.35959 12.49930 12.63859 12.76375 12.86104
##  [65] 12.95216 13.06255 13.18411 13.30873 13.42830 13.53471 13.61985 13.69414
##  [73] 13.77144 13.84884 13.92341 13.99225 14.05245 14.10107 14.13607 14.15929
##  [81] 14.17369 14.18223 14.18787 14.19356 14.20228 14.20990 14.21120 14.20742
##  [89] 14.19984 14.18970 14.17827 14.16681 14.15389 14.13743 14.11764 14.09475
##  [97] 14.06899 14.04058 14.00975 13.97652 13.94071 13.90229 13.86120 13.81741
## [105] 13.77087 13.72154
#n2
extract_n2
## `geom_smooth()` using formula 'y ~ x'

fit_n2
##   [1] 10.63701 10.87129 11.09782 11.31658 11.52757 11.73077 11.92617 12.11376
##   [9] 12.29351 12.46542 12.62949 12.78572 12.93412 13.07469 13.20744 13.33437
##  [17] 13.45659 13.57283 13.68181 13.78222 13.87279 13.95222 14.02371 14.09015
##  [25] 14.14981 14.20097 14.24189 14.27086 14.28616 14.27929 14.24767 14.19834
##  [33] 14.13837 14.07482 14.01477 13.96525 13.90851 13.82800 13.73265 13.63139
##  [41] 13.53317 13.44692 13.38156 13.31807 13.23687 13.14606 13.05374 12.96801
##  [49] 12.89699 12.84876 12.81395 12.77926 12.74691 12.71909 12.69804 12.68596
##  [57] 12.68506 12.70140 12.73621 12.78447 12.84115 12.90124 12.95971 13.01155
##  [65] 13.07171 13.15315 13.24784 13.34777 13.44490 13.53122 13.59870 13.66152
##  [73] 13.73539 13.81461 13.89347 13.96626 14.02728 14.07082 14.09979 14.12154
##  [81] 14.13702 14.14720 14.15302 14.15544 14.15541 14.15195 14.14361 14.13067
##  [89] 14.11341 14.09213 14.06709 14.03859 14.00641 13.97014 13.92973 13.88510
##  [97] 13.83622 13.78301 13.72542 13.66350 13.59738 13.52708 13.45263 13.37406
## [105] 13.29141 13.20471
#assign fits to a vector
n1_trend <- fit_n1
n2_trend <- fit_n2

#extract y min and max for each
limits_n1 <- ggplot_build(extract_n1)$data
## `geom_smooth()` using formula 'y ~ x'
limits_n1 <- as.data.frame(limits_n1)
n1_ymin <- limits_n1$ymin
n1_ymax <- limits_n1$ymax

limits_n2 <- ggplot_build(extract_n2)$data
## `geom_smooth()` using formula 'y ~ x'
limits_n2 <- as.data.frame(limits_n2)
n2_ymin <- limits_n2$ymin
n2_ymax <- limits_n2$ymax

#reassign dataframes (just to be safe)
work_n1 <- smooth_n1
work_n2 <- smooth_n2

#fill in missing dates to smooth fits
work_n1 <- work_n1 %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_n1 <- work_n1$date
work_n2 <- work_n2 %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_n2 <- work_n2$date

#create a new smooth dataframe to layer
smooth_frame_n1 <- data.frame(date_vec_n1, n1_trend, n1_ymin, n1_ymax)
smooth_frame_n2 <- data.frame(date_vec_n2, n2_trend, n2_ymin, n2_ymax)
#make plotlys

#plot smooth frames
p3 <- plotly::plot_ly() %>%
  plotly::add_lines(x = ~date_vec_n1, y = ~n1_trend,
                    data = smooth_frame_n1,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n1,
                                  '</br> Median Log Copies: ', round(n1_trend, digits = 2),
                                  '</br> Target: N1'),
                    line = list(color = '#1B9E77', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
plotly::add_lines(x = ~date_vec_n2, y = ~n2_trend,
                  data = smooth_frame_n2,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n2,
                                  '</br> Median Log Copies: ', round(n2_trend, digits = 2),
                                  '</br> Target: N2'),
                    line = list(color = '#D95F02', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
plotly::add_ribbons(x ~date_vec_n1, ymin = ~n1_ymin, ymax = ~n1_ymax,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n1, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(n1_ymax, digits = 2),
                                  '</br> Min Log Copies: ', round(n1_ymin, digits = 2),
                                  '</br> Target: N1'),
                    name = "",
                    line = list(color = '#1B9E77')) %>%
plotly::add_ribbons(x ~date_vec_n2, ymin = ~n2_ymin, ymax = ~n2_ymax,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n2, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(n2_ymax, digits = 2),
                                  '</br> Min Log Copies: ', round(n2_ymin, digits = 2),
                                  '</br> Target: N2'),
                    name = "",
                    line = list(color = '#D95F02')) %>%
                layout(yaxis = list(title = "Total Log SARS CoV-2 Copies", 
                                 showline = TRUE,
                                 automargin = TRUE)) %>%
                layout(xaxis = list(title = "Date")) %>%
    plotly::add_segments(x = as.Date("2020-06-24"), 
                                          xend = as.Date("2020-06-24"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "Bars Repoen",
                                          hoverinfo = "text",
                                          text = "</br> Bars Reopen",
                                                 "</br> 2020-06-24",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
    plotly::add_segments(x = as.Date("2020-07-09"), 
                                          xend = as.Date("2020-07-09"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "Mask Mandate",
                                          hoverinfo = "text",
                                          text = "</br> Mask Mandate",
                                                 "</br> 2020-07-09",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
    plotly::add_segments(x = as.Date("2020-08-20"), 
                                          xend = as.Date("2020-08-20"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "</br> Classes Begin",
                                                 "</br> 2020-08-20",
                                          hoverinfo = "text",
                                          text = "Classes Begin",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
  plotly::add_markers(x = ~date, y = ~log_sum_copies_L,
                      data = smooth_n1,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Observed Log Copies: ', round(log_sum_copies_L, digits = 2)),
                       marker = list(color = '#1B9E77', size = 6, opacity = 0.65)) %>%
    plotly::add_markers(x = ~date, y = ~log_sum_copies_L,
                      data = smooth_n2,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Observed Log Copies: ', round(log_sum_copies_L, digits = 2)),
                       marker = list(color = '#D95F02', size = 6, opacity = 0.65))

p3

Create final trend plot by stacking with epidemic curve

smooth_extract <-
    plotly::subplot(p3,p1, # plots to combine, top to bottom
      nrows = 2,
      heights = c(.6,.4),  # relative heights of the two plots
      shareX = TRUE,  # plots will share an X axis
      titleY = TRUE
    ) %>%
    # create a vertical "spike line" to compare data across 2 plots
    plotly::layout(
      xaxis = list(
        spikethickness = 1,
        spikedash = "dot",
        spikecolor = "black",
        spikemode = "across+marker",
        spikesnap = "cursor"
      ),
      yaxis = list(spikethickness = 0)
    )
## Warning: Ignoring 1 observations
smooth_extract
save(smooth_extract, file = "./smooth_extract.rda")